betafunctions (version 1.9.0)

mdlfit.gfx: Graphical presentation of model fit for the Beta-Binomial classification accuracy and consistency model.

Description

Tool for visually gauging the discrepancy between the observed and model-implied frequencies of observed-scores.

Usage

mdlfit.gfx(
  x,
  x.tickat = NULL,
  y.tickat = NULL,
  y.lim = NULL,
  main.lab = "Observed vs. Expected Frequencies",
  x.lab = "Bins",
  y.lab = "Frequency",
  x.grid = NULL,
  y.grid = NULL
)

Arguments

x

The output object from the LL.CA(), LL.MC.CA(), HB.CA(), or HB.CA.MC() functions.

x.tickat

The points along the x-axis that bins are to be labeled. Default is NULL (places a tick for each of the bins).

y.tickat

The points along the y-axis where frequencies are to be labelled. Default is NULL.

y.lim

The limits of the y-axis (frequencies). Useful for keeping the scale equal across several plots.

main.lab

The main label (title) of the plot.

x.lab

The label for the x-axis (the bins).

y.lab

The label for the y-axis (the frequencies).

x.grid

Control the vertical grid-lines of the plot. Takes NULL, NA, or a vector of values as input. If NULL, grid-lines are drawn automatically for each bin. If NA, no grid-lines are drawn. If a vector of values are supplied, lines are drawn at each value provided along the x-axis.

y.grid

Control the horizontal grid-lines of the plot. Takes NULL, NA, or a vector of values as input. If NULL, grid-lines are drawn automatically for each frequency (i.e., increments of 1). If NA, no grid-lines are drawn. If a vector of values are supplied, lines are drawn at each value provided along the y-axis.

Examples

# Generate some data. 1000 respondents taking 100 item test:
set.seed(060121)
p.success <- rBeta.4P(1000, 0.25, 0.75, 5, 3)
for (i in 1:100) {
  if (i == 1) {
   rawdata <- matrix(nrow = 1000, ncol = 100)
 }
 rawdata[, i] <- rbinom(1000, 1, p.success)
}

# Analyse the accuracy and consistency of the test and store the object:
out <- LL.CA(x = rowSums(rawdata), reliability = cba(rawdata), cut = 50,
min = 0, max = 100, modelfit = c(nbins = 20, minbin = 1))

# Feed the object to the mdlfit.gfx() function:
mdlfit.gfx(out)

# Given the number of observations, the y-axis ticks are a bit crowded. We
# can make it look less crowded by changing the number of ticks, labels, and
# the grid-lines:
mdlfit.gfx(out, y.tickat = seq(0, 250, 25), y.lim = c(0, 250),
y.grid = seq(0, 250, 12.5))